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Our paper presents a novel high dimensional probability density estimation technique using any dimensionality reduction method. Our method first performs subspace reduction using any matrix factorization algorithm and estimates the density in the low-dimensional space using sample-point variable bandwidth kernel density estimation. Subsequently, the high dimensional density is approximated from the...
In this paper, we propose a novel approach for detecting highlights in sports videos. The videos are temporally decomposed into a series of events based on an unsupervised event discovery and detection framework. The framework solely depends on easy-to-extract low-level visual features such as color histogram (CH) or histogram of oriented gradients (HOG), which can potentially be generalized to different...
We consider the problem of large-scale video classification. Our attention is focused on online video services since they can provide rich cross-video signals derived from user behavior. These signals help us to extract correlated information across videos which are co-browsed, co-uploaded, cocommented, co-queried, etc. Majority of the video classification methods omit this rich information and focus...
Automatic Language Identification (LID) in music has received significantly less attention than LID in speech. Here, we study the problem of LID in music videos uploaded on YouTube. We use a “bag-of-words” approach based on state-of-the-art content based audio-visual features and linear SVM classifiers for automatic LID. Our system obtains 48% accuracy for a corpus of 25000 music videos and 25 different...
We introduce a dynamical model for simultaneous registration and segmentation in a variational framework for image sequences, where the dynamics is incorporated using a Bayesian formulation. A linear stochastic equation relating the tracked object (or a region of interest) is first derived under the assumption that the successive images in the sequence are related by a dense and possibly non-linear...
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